Today

1. ggplot graphics
2. spatial (vector) data with sp package

ggplot2

The grammar of graphics: consistent aesthetics, multidimensional conditioning, and step-by-step plot building.

1. Data: The raw data
2. geom_: The geometric shapes representing data
3. aes(): Aesthetics of the geometric and statistical objects (color, size, shape, and position)
4. scale_: Maps between the data and the aesthetic dimensions

data
+ geometry,
+ aesthetic mappings like position, color and size
+ scaling of ranges of the data to ranges of the aesthetics

Additional settings

5. stat_: Statistical summaries of the data that can be plotted, such as quantiles, fitted curves (loess, linear models), etc.
6. coord_: Transformation for mapping data coordinates into the plane of the data rectangle
7. facet_: Arrangement of data into grid of plots
8. theme: Visual defaults (background, grids, axes, typeface, colors, etc.)

Simple scatterplot

library(ggplot2)
p <- ggplot(mtcars, aes(x=wt, y=mpg))
p + geom_point()

Aesthetic map: color by # of cylinders

p + 
  geom_point(aes(colour = factor(cyl)))

Set shape using # of cylinders

p + 
  geom_point(aes(shape = factor(cyl)))

Adjust size by qsec

p + 
  geom_point(aes(size = qsec))

Color by cylinders and size by qsec

p + 
  geom_point(aes(colour = factor(cyl),size = qsec))

Multiple aesthetics

p + 
  geom_point(aes(colour = factor(cyl),size = qsec,shape=factor(gear)))

Add a linear model

p + geom_point() + 
  geom_smooth(method="lm")

Change scale color

p + geom_point(aes(colour = cyl)) + 
  scale_colour_gradient(low = "blue")

Change scale shapes

p + geom_point(aes(shape = factor(cyl))) + 
  scale_shape(solid = FALSE)

Set aesthetics to fixed value

ggplot(mtcars, aes(wt, mpg)) + 
  geom_point(colour = "red", size = 3)

Transparancy: alpha=0.2

d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 0.2)

Varying alpha useful for large data sets

Transparancy: alpha=0.1

d + 
  geom_point(alpha = 0.1)

Transparancy: alpha=0.01

d + 
  geom_point(alpha = 0.01)

Building ggplots

Other Plot types

Your turn

Edit plot p above to include:

1. points
2. A smooth (‘loess’) curve
3. a “rug” to the plot

p+
  geom_point()+
  geom_smooth()+
  geom_rug()

Discrete X, Continuous Y

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_point()

Discrete X, Continuous Y + geom_jitter()

p + 
  geom_jitter()

Discrete X, Continuous Y + geom_violin()

p + 
  geom_violin()

Discrete X, Continuous Y + geom_violin()

p + 
  geom_violin() + geom_jitter(position = position_jitter(width = .1))

Three Variables

Will come back to this next week for raster package.

Stats

Visualize a data transformation

• Each stat creates additional variables with a common ..name.. syntax
• Often two ways: stat_bin(geom="bar") OR geom_bar(stat="bin")

2D kernel density estimation

Old Faithful Geyser Data on duration and waiting times.

library("MASS")
data(geyser)
m <- ggplot(geyser, aes(x = duration, y = waiting))

photo: Greg Willis

See ?geyser for details.

m + 
  geom_point()

m + 
  geom_point() +  stat_density2d(geom="contour")

Check ?geom_density2d() for details

m + 
  geom_point() +  stat_density2d(geom="contour") +
  xlim(0.5, 6) + ylim(40, 110)

Update limits to show full contours. Check ?geom_density2d() for details

m + stat_density2d(aes(fill = ..level..), geom="polygon") + 
  geom_point(col="red")

Check ?geom_density2d() for details

Your turn

Edit plot m to include:

• The point data (with red points) on top
• A binhex plot of the Old Faithful data

Experiment with the number of bins to find one that works.

See ?stat_binhex for details

m + stat_binhex(bins=10) + 
  geom_point(col="red")

Specifying Scales

Discrete color: default

b=ggplot(mpg,aes(fl))+
  geom_bar( aes(fill = fl)); b

Discrete color: greys

b + scale_fill_grey( start = 0.2, end = 0.8, 
                   na.value = "red")

Continuous color: defaults

a <- ggplot(mpg, aes(hwy)) + 
  geom_dotplot( aes(fill = ..x..)); a

Continuous color: gradient

a +  scale_fill_gradient( low = "red", 
                          high = "yellow")

Continuous color: gradient2

a + scale_fill_gradient2(low = "red", high = "blue", 
                       mid = "white", midpoint = 25)

Continuous color: gradientn

a + scale_fill_gradientn(
  colours = rainbow(10))

Discrete color: brewer

b + 
  scale_fill_brewer( palette = "Blues")

colorbrewer2.org

ColorBrewer: Diverging

ColorBrewer: Filtered

Your turn

Edit the contour plot of the geyser data to use a sequential brewer palette:

m +
  stat_density2d(aes(fill = ..level..), geom="polygon") + 
  geom_point(col="red")

Note: usescale_fill_distiller() rather than scale_fill_brewer() for continuous data

m + stat_density2d(aes(fill = ..level..), geom="polygon") + 
  geom_point(size=.75)+
  scale_fill_distiller(palette="OrRd",#breaks=c(0.005,0.008,0.01),
                       name="Kernel\nDensity")+
      xlim(0.5, 6) + ylim(40, 110)+
  xlab("Eruption Duration (minutes)")+
  ylab("Waiting time (minutes)")

Or use geom=tile for a raster representation.

m + stat_density2d(aes(fill = ..density..), geom="tile",contour=F) + 
  geom_point(size=.75)+
  scale_fill_distiller(palette="OrRd",
                       name="Kernel\nDensity")+
      xlim(0.5, 6) + ylim(40, 110)+
  xlab("Eruption Duration (minutes)")+
  ylab("Waiting time (minutes)")

Axis scaling

Create noisy exponential data

set.seed(201)
n <- 100
dat <- data.frame(
    xval = (1:n+rnorm(n,sd=5))/20,
    yval = 10^((1:n+rnorm(n,sd=5))/20)
)

Make scatter plot with regular (linear) axis scaling

sp <- ggplot(dat, aes(xval, yval)) + geom_point()
sp

Example from R Cookbook

log10 scaling of the y axis (with visually-equal spacing)

sp + scale_y_log10()

Coordinate Systems

Position

Stacked bars

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()

Dodged bars

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="dodge")